Dynamic Programming Approach for Valuing Options in the GARCH Model
提出一种基于动态规划和分段多项式逼近的高效算法，用于离散时间GARCH过程下的期权定价，适用于多种欧式和美式衍生品，数值实验表明该方法优于现有方法。
In this paper, we develop an efficient algorithm to value options under discrete-time GARCH processes. We propose a procedure based on dynamic programming coupled with piecewise polynomial approximation to compute the value of a given option, at all observation dates and levels of the state vector. The method can be used for the large GARCH family of models based on Gaussian innovations and may accommodate all low-dimensional European as well as American derivatives. Numerical implementations show that this method competes very advantageously with other available valuation methods.